.. _ray-oss-list: Community libraries that integrate with Ray =========================================== This page lists libraries that integrate with Ray for distributed execution. If you'd like to add your project to this list, feel free to file a pull request or open an issue on GitHub. Horovod ------- Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. [`Link to integration `__] MARS ---- Mars is a tensor-based unified framework for large-scale data computation which scales Numpy, Pandas and Scikit-learn. [:ref:`Link to integration `] Spacy ----- spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. [`Link to integration `__] Hugging Face Transformers ------------------------- State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. [`Link to integration `__] Seldon Alibi ------------ Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. Github: `https://github.com/SeldonIO/alibi `__ Intel Analytics Zoo ------------------- Analytics Zoo seamless scales TensorFlow, Keras and PyTorch to distributed big data (using Spark, Flink & Ray). [`Link to integration `__] Modin ----- Scale your pandas workflows by changing one line of code. Github: `https://github.com/modin-project/modin/ `__ ClassyVision ------------ An end-to-end framework for image and video classification. [`Link to integration `__] PyCaret ------- An open-source, low-code machine learning library in Python. Github: `https://github.com/pycaret/pycaret/ `_ Flambe ------ Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Github: `https://github.com/asappresearch/flambe `_